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Collection skeletons: declarative abstractions for data collections

Zhibo Li

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Tags: Computer science, Data Structures and Algorithms, OpenCL, performance portability, SYCL, Thesis

September 22, 2024 by hgpu

Abstractions for C++ code optimizations in parallel high-performance applications

Jiří Klepl, Adam Šmelko, Lukáš Rozsypal, Martin Kruliš

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Tags: Auto-Tuning, Computer science, CUDA, Data Structures and Algorithms, HPC, nVidia, Package, Tesla V100

August 25, 2024 by hgpu

Full-Scale File System Acceleration on GPU

Peter Maucher, Lennard Kittner, Nico Rath, Gregor Lucka, Lukas Werling, Yussuf Khalil, Thorsten Gröninger, Frank Bellosa

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Tags: AMD Radeon RX 6800, ATI, Computer science, Data Structures and Algorithms, HIP, Performance

March 24, 2024 by hgpu

Fully Concurrent GPU Data Structures

Muhammad Abdelghaffar Awad

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

August 28, 2022 by hgpu

stdgpu: Efficient STL-like Data Structures on the GPU

Patrick Stotko

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Package

August 25, 2019 by hgpu

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Elias Stehle, Hans-Arno Jacobsen

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GTX Titan X

June 5, 2019 by hgpu

Fast Locality Sensitive Hashing for Beam Search on GPU

Xing Shi, Shizhen Xu, Kevin Knight

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data Structures and Algorithms, Hashing, NLP, nVidia, Tesla K20

June 9, 2018 by hgpu

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Matthias Springer, Hidehiko Masuhara

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Tags: Computer science, CUDA, Data Structures and Algorithms, DSL, nVidia, Package

February 9, 2018 by hgpu

Efficient Parallel Strategy Improvement for Parity Games

John Fearnley

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Logic in Computer Science, nVidia, nVidia GeForce GTX 780, Package

May 9, 2017 by hgpu

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, Herve Jegou

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Package

March 5, 2017 by hgpu

An Efficient Multiway Mergesort for GPU Architectures

Henri Casanova, John Iacono, Ben Karsin, Nodari Sitchinava, Volker Weichert

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Sorting, Tesla K40

February 28, 2017 by hgpu

Trie Compression for GPU Accelerated Multi-Pattern Matching

Xavier Bellekens, Amar Seeam, Christos Tachtatzis, Robert Atkinson

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Tags: Algorithms, Compression, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 1080

February 18, 2017 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Collection skeletons: declarative abstractions for data collections

Abstractions for C++ code optimizations in parallel high-performance applications

Full-Scale File System Acceleration on GPU

Fully Concurrent GPU Data Structures

stdgpu: Efficient STL-like Data Structures on the GPU

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Fast Locality Sensitive Hashing for Beam Search on GPU

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Efficient Parallel Strategy Improvement for Parity Games

Billion-scale similarity search with GPUs

An Efficient Multiway Mergesort for GPU Architectures

Trie Compression for GPU Accelerated Multi-Pattern Matching

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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